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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14925 |
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| _version_ | 1866914880090537984 |
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| author | Zhang, He Wu, Chuhao Xie, Jingyi Rubino, Fiona Graver, Sydney Kim, ChanMin Carroll, John M. Cai, Jie |
| author_facet | Zhang, He Wu, Chuhao Xie, Jingyi Rubino, Fiona Graver, Sydney Kim, ChanMin Carroll, John M. Cai, Jie |
| contents | Qualitative research, renowned for its in-depth exploration of complex phenomena, often involves time-intensive analysis, particularly during the coding stage. Existing software for qualitative evaluation frequently lacks automatic coding capabilities, user-friendliness, and cost-effectiveness. The advent of Large Language Models (LLMs) like GPT-3 and its successors marks a transformative era for enhancing qualitative analysis. This paper introduces QualiGPT, a tool developed to address the challenges associated with using ChatGPT for qualitative analysis. Through a comparative analysis of traditional manual coding and QualiGPT's performance on both simulated and real datasets, incorporating both inductive and deductive coding approaches, we demonstrate that QualiGPT significantly improves the qualitative analysis process. Our findings show that QualiGPT enhances efficiency, transparency, and accessibility in qualitative coding. The tool's performance was evaluated using inter-rater reliability (IRR) measures, with results indicating substantial agreement between human coders and QualiGPT in various coding scenarios. In addition, we also discuss the implications of integrating AI into qualitative research workflows and outline future directions for enhancing human-AI collaboration in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14925 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When Qualitative Research Meets Large Language Model: Exploring the Potential of QualiGPT as a Tool for Qualitative Coding Zhang, He Wu, Chuhao Xie, Jingyi Rubino, Fiona Graver, Sydney Kim, ChanMin Carroll, John M. Cai, Jie Human-Computer Interaction Qualitative research, renowned for its in-depth exploration of complex phenomena, often involves time-intensive analysis, particularly during the coding stage. Existing software for qualitative evaluation frequently lacks automatic coding capabilities, user-friendliness, and cost-effectiveness. The advent of Large Language Models (LLMs) like GPT-3 and its successors marks a transformative era for enhancing qualitative analysis. This paper introduces QualiGPT, a tool developed to address the challenges associated with using ChatGPT for qualitative analysis. Through a comparative analysis of traditional manual coding and QualiGPT's performance on both simulated and real datasets, incorporating both inductive and deductive coding approaches, we demonstrate that QualiGPT significantly improves the qualitative analysis process. Our findings show that QualiGPT enhances efficiency, transparency, and accessibility in qualitative coding. The tool's performance was evaluated using inter-rater reliability (IRR) measures, with results indicating substantial agreement between human coders and QualiGPT in various coding scenarios. In addition, we also discuss the implications of integrating AI into qualitative research workflows and outline future directions for enhancing human-AI collaboration in this field. |
| title | When Qualitative Research Meets Large Language Model: Exploring the Potential of QualiGPT as a Tool for Qualitative Coding |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.14925 |